Mountain wind power equipment transportation path planning and hoisting control method and system

Abstract

The invention relates to the technical field of path planning, and discloses a mountain wind power equipment transportation path planning and hoisting control method and system. The method comprises the following steps: scanning a mountain road through a laser ranging array to obtain a road cross section database, and modeling to obtain blade scanning envelope data; performing dynamic path planning by adopting a terrain adaptability predictive control algorithm, and obtaining a safe transportation path according to stress constraint verification; the sensor array monitors the blade state in real time and corrects the track; a temperature sensor collects tower drum temperature data, and the size change is predicted through a thermal deformation compensation mechanism; and the positioning system obtains hoisting reference coordinates and controls the hoisting posture in combination with the compensation parameters. The technical problems that dynamic sweeping modeling, stress constraint verification and real-time track correction are lacked in wind power equipment transportation path planning in the mountain environment, and thermal deformation compensation and multi-system collaborative optimization are lacked in hoisting control are solved. And the transportation safety and the hoisting control precision of the mountain wind power equipment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path planning, in particular to a mountain wind power equipment transportation path planning and hoisting control method and system. BACKGROUND

[0002] With the rapid development of the wind power industry, the transportation and hoisting technology of large-scale wind power equipment in complex mountainous terrain has become a key link in the construction of wind farms. The existing wind power equipment transportation mainly adopts traditional road survey and manual path planning method, and the transportation route is determined through GPS navigation and experience judgment, and the hoisting operation relies on the skill level and on-site experience of the operator to position and adjust the equipment posture. The traditional method has formed a relatively mature operation process in the construction of flat terrain wind farms, which can meet the transportation and installation needs of conventional wind power equipment.

[0003] However, the existing technology has significant deficiencies in mountain wind power equipment transportation path planning and hoisting control. First, the traditional path planning method lacks accurate modeling of the dynamic space occupation of the super-long blade, and cannot accurately predict the sweeping range of the blade in the mountain curve transportation, resulting in frequent safety hazards of blade collision with the mountain during transportation. Secondly, the existing path planning algorithm does not consider the mechanical properties of the blade material, ignoring the influence of path curvature on the stress of the blade root, which can easily cause damage to the blade structure. In addition, the traditional hoisting control lacks a compensation mechanism for temperature changes in mountainous environments, and the thermal expansion and contraction of the steel structure tower under large temperature differences will affect the hoisting precision and reduce the equipment installation quality.

[0004] Due to the lack of dynamic sweeping modeling of the blade, the space conflict problem is caused, which further leads to the safety path planning problem that needs to consider the stress constraints of the blade material, and further leads to the problem of real-time state monitoring and trajectory dynamic correction during transportation, and finally extends to the precise positioning problem that needs to consider thermal deformation compensation and multi-system collaborative control in the hoisting stage. These problems are interrelated and progressive, forming a complete technical chain of mountain wind power equipment transportation path planning and hoisting control, which needs to be solved by a systematic technical solution. SUMMARY

[0005] The present application provides a mountain wind power equipment transportation path planning and hoisting control method and system, which solves the technical problems of lack of dynamic sweeping modeling, stress constraint verification and real-time trajectory correction in wind power equipment transportation path planning in mountainous environments, and lack of thermal deformation compensation and multi-system collaborative optimization in hoisting control. The safety of mountain wind power equipment transportation and the precision of hoisting control are improved.

[0006] In a first aspect, the present application provides a mountain wind power equipment transportation path planning and hoisting control method, which comprises: The road cross-section database is obtained by performing three-dimensional scanning on the mountain road through a laser ranging array, and the blade swept envelope data is obtained by modeling the spatial occupation of the wind turbine blade according to the geometric parameters of the wind turbine blade. The safety transport path is obtained by performing safety verification on the blade transport path according to the stress constraint condition. The dynamic correction path is obtained by performing trajectory correction on the safety transport path according to the blade state data. The tower compensation parameters are obtained by performing size prediction on the tower temperature data according to the thermal deformation compensation mechanism. The equipment installation position is obtained by controlling the lifting posture according to the tower compensation parameters and the dynamic correction path.

[0007] In a second aspect, the present application provides a mountain wind power equipment transport path planning and lifting control system, which comprises: The road cross-section database is obtained by performing three-dimensional scanning on the mountain road through a laser ranging array, and the blade swept envelope data is obtained by modeling the spatial occupation of the wind turbine blade according to the geometric parameters of the wind turbine blade. The safety transport path is obtained by performing safety verification on the blade transport path according to the stress constraint condition. The dynamic correction path is obtained by performing trajectory correction on the safety transport path according to the blade state data. The tower compensation parameters are obtained by performing size prediction on the tower temperature data according to the thermal deformation compensation mechanism. The equipment installation position is obtained by controlling the lifting posture according to the tower compensation parameters and the dynamic correction path.

[0008] In a third aspect, a mountain wind power equipment transportation path planning and hoisting control device is provided, comprising a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to enable the mountain wind power equipment transportation path planning and hoisting control device to perform the mountain wind power equipment transportation path planning and hoisting control method described above.

[0009] In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing instructions, when executed on a computer, causing the computer to perform the mountain wind power equipment transportation path planning and hoisting control method described above.

[0010] In the technical solution provided in the present application, the road cross-section database is obtained by three-dimensional scanning of the mountain road through the laser ranging array, and the blade swept envelope data is obtained by modeling according to the geometric parameters of the wind power blade, solving the technical problem of lack of accurate modeling of the dynamic space occupation of the super-long blade in the prior art. The terrain adaptability predictive control algorithm performs dynamic path planning on the road data and the blade data, and verifies the safety according to the stress constraint condition, overcoming the limitation of the traditional path planning algorithm that cannot process the mechanical properties of the blade material. The sensor array monitors the blade state in real time and corrects the trajectory according to the state data, realizing dynamic optimization of the transportation process and making up for the lack of real-time feedback mechanism in the prior art. The temperature sensor collects tower temperature data and performs size prediction through the thermal deformation compensation mechanism, effectively solving the problem of the influence of temperature difference in mountainous environment on the size accuracy of steel structure. The positioning system obtains the hoisting reference coordinates and combines the tower compensation parameters and the dynamically corrected path to control the hoisting posture, realizing the collaborative optimization of the whole process of transportation and hoisting, and breaking through the technical bottleneck of the prior art that the two links are processed separately.

[0011] The application of the terrain adaptability predictive control algorithm in the mountain wind power equipment transportation path planning fully considers the special constraints of complex mountain terrain on the transportation of super-long blades. The algorithm can find the optimal path under the premise of ensuring traffic safety through spatial matching analysis and multi-objective optimization calculation, and has stronger terrain adaptability compared with the traditional MPC algorithm. The introduction of blade root stress calculation and stress threshold comparison makes the path planning not only consider geometric constraints, but also fully consider the mechanical properties of the blade material, avoiding structural damage during transportation. The application of the thermal deformation compensation mechanism in the tower hoisting control solves the size change problem of the steel structure in the mountainous large temperature difference environment. Through time series prediction and matching verification, the tower state at the hoisting moment can be calculated in advance, ensuring the hoisting accuracy. The combination of the crane kinematics inverse solution algorithm and the six-degree-of-freedom control matrix realizes the accurate control of the hoisting posture. Through dynamic school verification and boundary constraint processing, the safety and reliability of the hoisting process are guaranteed, and the precision and consistency are higher compared with the traditional manual experience operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a schematic diagram of an embodiment of a method for mountain wind power equipment transportation path planning and hoisting control in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a mountain wind power equipment transportation path planning and hoisting control system in an embodiment of the present application; Figure 3 It is a schematic block diagram of the structure of the mountain wind power equipment transportation path planning and hoisting control equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a method and system for transport path planning and hoisting control of mountain wind power equipment. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0015] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for transporting and controlling mountain wind power equipment routes includes: Step S101: Perform three-dimensional scanning of mountain roads using a laser ranging array to obtain a road cross-section database, model the blade space occupancy based on wind turbine blade geometric parameters, and obtain blade sweep envelope data; Step S102: Dynamically planning the road cross-section database and the blade sweep envelope data using a terrain-adaptive predictive control algorithm to obtain a blade transport path, and performing safety verification on the blade transport path based on stress constraints to obtain a safe transport path. In step S103, the blade transportation process is monitored in real time through the sensor array to obtain blade state data, and the safety transportation path is corrected in trajectory according to the blade state data to obtain a dynamic correction path. In step S104, the temperature of each section of the tower drum is collected through a temperature sensor to obtain tower drum temperature data, and the tower drum temperature data is predicted in size according to a thermal deformation compensation mechanism to obtain tower drum compensation parameters. In step S105, the coordinates of the hoisting site are obtained through a positioning system to obtain hoisting reference coordinates, and the hoisting posture is controlled according to the tower drum compensation parameters and the dynamic correction path to obtain an equipment installation position.

[0016] It can be understood that the execution subject of the present application can be a mountain wind power equipment transportation path planning and hoisting control system, and can also be a terminal or a server, which is not limited here. The server is taken as an example for description in the embodiments of the present application.

[0017] Specifically, the mountain road is scanned in three dimensions through a laser ranging array, and the laser radar emits laser beams to measure the three-dimensional coordinates of the ground reflection points every 10 meters along the road. Each laser in the laser ranging array scans according to a preset scanning angle range, and the obtained laser reflection points are associated in time sequence and spatial position to form road sampling point cloud data containing X, Y and Z coordinate information. Then, the road sampling point cloud data is extracted in cross-sectional profile, and the ground boundary points and mountain contour points on each sampling section are identified to connect these boundary points in elevation order to form road cross-sectional profile data of each sampling point, and then the road cross-sectional profile data is sorted and stored according to the road mileage marker to establish a road cross-sectional database containing mileage marker, cross-sectional width and elevation information. The wind turbine blade is modeled according to the length, width and thickness of the blade, and a three-dimensional geometric model of the blade is constructed. The model uses a mathematical description method of the blade profile line, and the variable cross-section characteristics of the blade from the root to the tip are represented by a segmented function. Then, the dynamic swing range of the blade during turning is calculated according to the turning radius of the transportation vehicle, and the maximum deflection angle of the blade relative to the vehicle body during turning is analyzed to calculate the blade sweep envelope data, which contains the spatial occupation boundary of the blade under different turning radii.

[0018] The terrain adaptive predictive control algorithm dynamically plans the path by processing the road cross-section database and the blade swept envelope data. The algorithm spatially matches each cross-section in the road cross-section database with the blade swept envelope data, calculates the minimum distance between the blade swept boundary and the road boundary to determine the feasibility of passing each road segment, generates a path feasibility matrix, and the values in the matrix represent the passing safety factor of the road segment. Based on the path feasibility matrix, a multi-objective optimization calculation is performed, which considers multiple objective functions such as transportation distance, path safety, and passing time. The weighted sum method is used to convert the multi-objective problem into a single-objective optimization problem, and the genetic algorithm is used to search for a candidate path set. The curvature radius of each path in the candidate path set is calculated, the curvature radius of each road segment is calculated based on the geometric relationship of the adjacent three points on the path, and the path curvature parameter is obtained. Then, the blade root stress is calculated based on the blade length and the elastic modulus of the blade material, the stress distribution of the blade under bending load is calculated using beam bending theory, and the path stress distribution data is obtained. The path stress distribution data is compared with the preset stress threshold to select the path that meets the stress constraint condition as the blade transportation path, and the path is then smoothed and optimized considering the mountain crosswind influence factor to obtain a safe transportation path.

[0019] The sensor array monitors the blade transportation process in real time. Strain sensors installed at the root, middle, and tip of the blade collect strain data during transportation. The strain sensors convert the small deformation of the blade surface into an electrical signal, which is then converted into a digital strain value by a signal conditioning circuit. Based on the strain data, the bending stress is calculated. According to the linear relationship between stress and strain in material mechanics, the strain value is multiplied by the elastic modulus of the blade material to obtain the real-time stress distribution of the blade. The real-time stress distribution of the blade is compared with the preset stress threshold. When the stress value exceeds the threshold, a stress overrun warning signal is generated. The warning signal is associated with the current location coordinates of the transportation vehicle to form blade state data by combining the location information of the stress overrun with the warning signal. Based on the blade state data, the risk road segments of the safe transportation path are identified. By analyzing the characteristics of the road segments where stress overrun occurs, high-risk path nodes that are prone to cause stress concentration are identified. These nodes are then input into the path re-planning algorithm, which seeks alternative trajectories that avoid high-risk nodes while keeping the start and end points unchanged. A revised trajectory scheme is generated, and finally, the revised scheme is optimized based on transportation time cost and safety factor to obtain a dynamic revised path.

[0020] The temperature sensor collects the temperature of each section of the tower drum. The distributed temperature sensor is arranged on the surface of the bottom section, middle section and top section of the tower drum. The sensor converts the temperature change into a voltage signal by using the thermocouple principle. The temperature values of each section of the tower drum are recorded in real time by the data acquisition system. The temperature gradient analysis is performed based on the temperature values of each section of the tower drum. The temperature difference value and the distance ratio between adjacent measuring points are calculated to obtain the tower drum temperature distribution curve. The curve reflects the temperature change rule of the tower drum along the height direction. The temperature distribution curve of the tower drum is subtracted from the ambient reference temperature to obtain the tower drum temperature data. Then, the thermal expansion amount is calculated based on the temperature data and the linear expansion coefficient of the steel material. The length change value of each section of the tower drum is calculated by using the linear expansion formula. The length change value of each section is multiplied by three parameters, i.e. the temperature difference value, the original length and the expansion coefficient, to obtain the length change amount. The cumulative deformation analysis is performed based on the length change value of each section. The length change values of each section are added in the order of the tower drum from bottom to top to obtain the overall size deviation of the tower drum. The deviation is input into the thermal deformation compensation mechanism to calculate the compensation amount and obtain the tower drum compensation parameter.

[0021] The positioning system obtains the coordinates of the hoisting site. The GPS-RTK positioning system measures the coordinates of the center point of the hoisting site foundation by receiving satellite signals and ground base station differential signals to obtain the centimeter-level precision of the foundation center coordinates. Then, the hoisting operation coordinate system is established with the coordinates as the origin to obtain the hoisting reference coordinates. The deviation correction calculation is performed on the tower drum compensation parameter and the hoisting reference coordinates. The installation position is corrected according to the size change caused by the thermal deformation to obtain the tower drum installation correction coordinates. The accuracy of the correction coordinates is verified according to the equipment arrival state information recorded in the dynamic correction path to ensure the accuracy of the installation coordinates. The spatial positioning control is performed on the crane hook position based on the installation coordinates. The three-dimensional deviation between the current position and the target position of the hook is calculated. The hook target position is planned by planning the motion trajectory of the hook. The position is input into the crane attitude control system. The target angle of each joint is calculated by using the inverse kinematics algorithm. The dynamic verification is performed by considering the load state of the crane to generate the six-degree-of-freedom hoisting attitude control instruction including the pitch angle, rotation angle, amplitude angle and lifting control. The control of the equipment installation position is realized.

[0022] In a specific embodiment, the process of step S101 can specifically include the following steps: Laser scanning sampling processing is performed on every 10-meter interval along the mountain road to obtain road sampling point cloud data. Cross-section profile extraction processing is performed based on the road sampling point cloud data to obtain road cross-section profile data of each sampling point. The road cross-section profile data is sorted and stored according to the road mileage marker to obtain a road cross-section database. The blade geometry model construction processing is performed based on the wind turbine blade length, blade width and blade thickness parameters to obtain a blade three-dimensional geometry model, and the blade three-dimensional geometry model is calculated for a dynamic swing range according to a turning radius of a transport vehicle to obtain blade swept envelope data. The blade swept envelope data is processed for spatial coordinate transformation to obtain blade swept envelope data matched with the road cross-section database.

[0023] Specifically, the laser scanning sampling processing collects data at every 10-meter interval along the mountain road by a laser ranging array, the laser ranging array includes multiple lasers, each laser emits a laser beam in a different direction, the laser beam is reflected back to the sensor after encountering the ground or mountain surface, the sensor calculates the distance according to the round-trip time of the laser and records the laser emission angle, and the distance and angle information are converted into three-dimensional coordinates to form road sampling point cloud data. The road sampling point cloud data includes X coordinate, Y coordinate, Z coordinate and corresponding milepost information of each reflection point, and the point cloud data is densely distributed on the road surface and the two sides of the mountain, reflecting the true three-dimensional shape of the mountain road. The cross-section profile extraction processing groups the road sampling point cloud data according to the milepost, each group of point cloud data represents the terrain information at the same cross-section position, and connects the key points in the point cloud according to the elevation from low to high to form the road cross-section profile data of each sampling point, which includes the width of the cross-section, the angle of the side slope on both sides and the elevation of the road surface.

[0024] The road cross-section profile data sorting storage processing arranges the cross-section profile data of each sampling point in the order of the road milepost from the starting point to the ending point, and establishes a road cross-section database including the milepost, the cross-section width, the left boundary coordinate, the right boundary coordinate and the road centerline elevation. The database uses a relational data structure, each record corresponds to a cross-section, and the record includes the complete geometric parameters of the cross-section. The database supports fast retrieval by milepost and filtering by geometric parameters.

[0025] The blade geometry model construction processing is performed based on the wind turbine blade length, blade width and blade thickness parameters to obtain a blade three-dimensional geometry model, and the blade three-dimensional geometry model is calculated for a dynamic swing range according to a turning radius of a transport vehicle to obtain blade swept envelope data.

[0026] The dynamic swing range calculation process analyzes the change of the space occupied by the blade during transportation according to the turning radius of the transport vehicle, the turning radius of the transport vehicle refers to the radius of curvature of the vehicle gravity center track when the front wheels of the vehicle are turned, the blade is fixed at the rear of the vehicle, and the blade will be deflected relative to the vehicle longitudinal axis when the vehicle turns. The calculation process determines the position of the connecting point of the blade and the vehicle, and then calculates the maximum deflection distance of the tail end of the blade according to the turning radius of the vehicle and the length of the blade. Considering the bending deformation of the blade itself, the maximum space range swept by the blade during turning is calculated, and the blade swept envelope data is obtained. The blade swept envelope data describes the three-dimensional space boundary occupied by the blade under various turning conditions, including maximum swept width, swept height, swept length and other parameters.

[0027] The space coordinate transformation process converts the blade swept envelope data from the vehicle coordinate system to the road coordinate system. The vehicle coordinate system takes the vehicle gravity center as the origin, the vehicle forward direction as the X axis, the vehicle left side as the Y axis, and the vertical upward as the Z axis. The road coordinate system takes the road starting point as the origin, the road forward direction as the X axis, the road left side as the Y axis, and the road surface vertical upward as the Z axis. The coordinate transformation process needs to determine the position and attitude of the vehicle on the road. According to the current milepost number and lateral offset of the vehicle, the translation matrix of the vehicle coordinate system relative to the road coordinate system is calculated. According to the angle between the vehicle driving direction and the road tangent direction, the rotation matrix is calculated. The translation matrix and the rotation matrix are applied to each coordinate point in the blade swept envelope data to obtain the blade swept envelope data matched with the road cross section database. The transformed blade swept envelope data and the road cross section database use a unified coordinate system, and the relative position relationship between the blade swept boundary and the road boundary is directly compared to determine the trafficability of the blade transportation.

[0028] In a specific embodiment, the process of performing step S102 can specifically include the following steps: The road cross section database and the blade swept envelope data are input into the terrain adaptability prediction control algorithm for spatial matching analysis processing to obtain a path feasibility matrix. Based on the path feasibility matrix, multi-objective optimization calculation processing is performed to obtain a candidate path set; The curvature radius of each path in the candidate path set is calculated to obtain path curvature parameters. The path curvature parameters are subjected to blade root stress calculation processing according to the length of the blade and the elastic modulus of the blade material to obtain path stress distribution data; Based on the path stress distribution data, stress threshold comparison processing is performed to obtain stress safety evaluation results. Paths that meet the stress constraint conditions are screened to obtain a blade transportation path; The blade transportation path is subjected to path smoothing optimization processing to obtain an optimized transportation path. The safety margin verification processing is performed on the optimized transportation path according to the mountain crosswind influence factor to obtain a safe transportation path.

[0029] Specifically, the terrain-adaptive predictive control algorithm is a path planning algorithm specially designed for complex mountainous terrain, which performs spatial matching analysis processing on the road cross-section database and the blade swept envelope data as input. The spatial matching analysis compares the cross-section geometric parameters corresponding to each milepost number in the road cross-section database with the spatial occupancy boundary at the same position in the blade swept envelope data one by one, calculates the minimum distance between the blade swept boundary and the left and right boundaries of the road, and records it as passable when the minimum distance is greater than the safety threshold, and as impassable when the minimum distance is less than the safety threshold. The passability judgment results of all milepost numbers form a path feasibility matrix. The path feasibility matrix is a two-dimensional array, with rows representing different milepost positions and columns representing different lateral offsets. The matrix element value represents the passing safety coefficient at that position, and the higher the safety coefficient, the safer the passing. Multi-objective optimization calculation processing finds the optimal path combination from the starting point to the ending point based on the path feasibility matrix. This calculation process considers multiple optimization objectives such as shortest path length, highest passing safety coefficient, and largest turning radius. The weighted summation method is used to combine multiple objective functions into a single evaluation function. The genetic algorithm is used to search for the optimal solution in the feasible solution space, generating a set of candidate paths that meet the constraint conditions.

[0030] The curvature radius calculation process performs geometric analysis on each path in the candidate path set. The curvature radius is an important parameter that describes the degree of path curvature. The calculation method is to select three consecutive points on the path and calculate the curvature radius based on the circular arc formed by these three points. The smaller the curvature radius, the more curved the path. The calculation process traverses all points on the path, calculates the corresponding curvature radius value for each point, and arranges all curvature radius values according to the path mileage to form a path curvature parameter. The path curvature parameter contains the curvature radius value of each path point and the corresponding milepost number, reflecting the bending change law of the entire path. The blade root stress calculation process performs mechanical analysis on the path curvature parameter based on the blade length and the elastic modulus of the blade material. When the transport vehicle travels along the curved path, the blade will be subjected to centrifugal force and will bend and deform. The bending moment and stress at the blade root are the largest. The calculation process calculates the centrifugal acceleration when the vehicle turns according to the path curvature radius, then calculates the distributed load on the blade according to the blade length and mass distribution, and then calculates the bending moment and stress at the blade root using the bending theory of beams. The influence of the elastic modulus of the blade material on the stress distribution needs to be considered in the calculation. The greater the elastic modulus of the material, the smaller the stress produced under the same load. The blade root stress values of all points on the path are arranged in order of mileage to form path stress distribution data.

[0031] The stress threshold comparison process compares each stress value in the path stress distribution data with a preset stress threshold, which is a safety limit value determined according to the allowable stress of the blade material, and determines that it is unsafe when the calculated stress value exceeds the stress threshold. The comparison process traverses all stress values in the path stress distribution data, counts the number of points exceeding the threshold and the degree of over-limit, calculates the overall safety evaluation index of the path, and obtains the stress safety evaluation result. The stress safety evaluation result includes parameters such as the maximum stress value, the number of over-limit points, and the safety margin of the path, reflecting the degree of influence of the path on the safety of the blade structure. The path screening process selects a path that meets the stress constraint condition from the candidate path set according to the stress safety evaluation result, and the screening conditions include that the maximum stress value does not exceed the threshold, the number of over-limit points is zero, and the safety margin is greater than the minimum requirement, etc. The blade transportation path is obtained through screening.

[0032] The path smoothing optimization process geometrically optimizes the blade transportation path to eliminate sharp corners and discontinuous points in the path. The smoothing optimization uses a spline curve fitting method to replace the polyline segments in the original path with smooth curves while keeping the start and end points unchanged, reducing the curvature change rate of the path and reducing the impact and vibration during vehicle driving. The optimization process identifies areas in the path where the curvature changes greatly, then inserts intermediate control points in these areas, connects all control points with cubic spline curves, and generates the optimized transportation path. The mountain crosswind influence factor is a correction parameter considering the influence of mountain terrain on wind field distribution. Mountain terrain can change the direction and speed of wind flow. Wind speed will significantly increase in special terrains such as ridges, valleys, and canyons, and wind direction will also deflect. The safety margin verification process evaluates the risk of the optimized transportation path according to the mountain crosswind influence factor, calculates the influence of wind load on blade transportation safety at each point on the path, and adjusts the path or adds windproof measures when the wind load exceeds the vehicle's rollover resistance. The verification process associates the terrain characteristics of each point on the path with the wind field data, calculates the crosswind influence factor of the point, and then calculates the safety margin based on the wind-affected area of the blade and the stability parameters of the vehicle. When the safety margin of all path points meets the requirements, a safe transportation path is obtained.

[0033] In a specific embodiment, the process of performing step S103 can specifically include the following steps: The strain sensors installed at the root, middle and tip of the blade are used to collect and process the deformation state of the blade in real time to obtain blade strain data. Based on the blade strain data, the bending stress calculation process is performed to obtain the real-time stress distribution of the blade. The real-time stress distribution of the blade is compared and analyzed with the preset stress threshold to obtain a stress over-limit early warning signal. The stress over-limit early warning signal is associated with the current position coordinates of the transportation vehicle to obtain blade state data. The risk section of the safe transportation path is identified based on the blade state data, a high-risk path node is obtained, the high-risk path node is input into a path re-planning algorithm for generating a candidate trajectory, and a modified trajectory scheme is obtained. The modified trajectory scheme is subjected to path feasibility verification processing to obtain a verified modified scheme, and the verified modified scheme is subjected to optimization processing according to a transportation time cost and a safety coefficient to obtain a dynamic modified path.

[0034] Specifically, the strain sensor is a sensor capable of converting object deformation into an electrical signal. The strain sensor installed at the root, middle and tip of the blade collects the deformation state of the blade in real time. When the blade is bent and deformed, the strain gauge pasted on the surface of the blade will deform together with the blade, and the resistance value of the strain gauge will change. The amount of change is proportional to the strain of the blade. During data acquisition, the strain sensor collects the strain value of the blade surface at a frequency of 100 times per second. After converting the analog signal into a digital signal, it is transmitted to the data processing unit to form blade strain data containing time stamp, position information and strain value. The bending stress calculation processing is based on blade strain data for mechanical analysis. According to Hooke's law, stress is equal to strain multiplied by the elastic modulus of the material. In the calculation process, the strain value at each sensor position is multiplied by the elastic modulus of the blade glass steel material to obtain the stress value at that position. Then, the stress distribution at any position on the blade surface is calculated by an interpolation algorithm to form the real-time stress distribution of the blade. The real-time stress distribution of the blade contains the stress value at each position of the blade from the root to the tip and the corresponding time information, reflecting the stress state change of the blade during transportation.

[0035] Each stress value in the real-time stress distribution of the blade is compared with the preset stress threshold in the comparative analysis processing. The preset stress threshold is the upper limit of the allowable stress determined according to the fatigue limit of the blade material and the safety factor. When the real-time stress value exceeds the preset threshold, it is determined that the stress is out of limit, and a stress out-of-limit early warning signal containing the out-of-limit position, out-of-limit degree and out-of-limit time is generated. The position association processing fuses the stress out-of-limit early warning signal with the current position coordinates of the transportation vehicle. The current position coordinates of the transportation vehicle are obtained by GPS positioning and contain longitude, latitude and altitude information. In the position association process, the time of stress out-of-limit occurrence is matched with the position trajectory recorded by GPS to determine the specific geographic location of stress out-of-limit occurrence. The out-of-limit information and the position information are combined to form the blade state data. The blade state data contains information such as stress out-of-limit section mileage, out-of-limit stress value, duration and terrain characteristics, describing the safety state of the blade in a specific section.

[0036] The risk section identification process analyzes the dangerous areas prone to causing blade stress concentration in the safe transportation path based on the blade state data. The identification process finds out the sections with high stress overrun frequency and large overrun degree by statistically analyzing the spatial distribution characteristics of stress overrun events. These sections usually correspond to sharp bends, steep slopes, and poor road conditions such as uneven road surface. The identification algorithm divides the path into grids according to the mileage, and counts the number of stress overruns and the average overrun degree in each grid. When the risk evaluation index of a grid exceeds the set threshold, it is marked as a high-risk path node. The path re-planning algorithm is a dynamic path search algorithm that treats high-risk path nodes as obstacles that need to be avoided. It searches for alternative paths while keeping the start and end points unchanged. The algorithm uses the A-star search method and evaluates the path length and traffic safety. It searches for the optimal detour route from the current position to the target position and generates multiple alternative trajectory schemes. Each trajectory scheme includes different path options for avoiding high-risk nodes, and each trajectory scheme is labeled with parameters such as path length, estimated travel time, safety risk level, etc.

[0037] The path feasibility verification process performs technical feasibility tests on the alternative trajectory schemes. The verification process includes geometric feasibility testing and dynamic feasibility testing. The geometric feasibility testing checks whether the turning radius in the trajectory meets the minimum turning radius requirement for blade transportation and whether the trajectory width exceeds the road passable width. The dynamic feasibility testing analyzes the stability and safety of the vehicle when driving on the trajectory and calculates whether the lateral and longitudinal accelerations at each point on the trajectory are within the vehicle performance range. The verification process selects trajectory schemes that meet all the constraint conditions to form the verified modified scheme. The optimization process evaluates and sorts the verified modified schemes based on transportation time cost and safety factor. The transportation time cost is calculated by dividing the trajectory length by the average driving speed, and the safety factor is evaluated by factors such as minimum turning radius, maximum slope, and road conditions. The optimization algorithm uses a multi-attribute decision-making method to weight and sum the time cost and safety factor according to different weights, and selects the scheme with the highest comprehensive evaluation value as the dynamic correction path. The dynamic correction path is a real-time optimized transportation path that can avoid high-risk sections found during transportation, ensuring the safety of blade transportation.

[0038] In a specific embodiment, the process of performing step S104 can specifically include the following steps: Real-time temperature acquisition and processing of the tower drum bottom section, middle section, and top section are performed by distributed temperature sensors to obtain tower drum temperature values for each section. Temperature gradient analysis is performed based on the tower drum temperature values to obtain a tower drum temperature distribution curve. The tower drum temperature distribution curve is subtracted from the environmental reference temperature to obtain tower drum temperature data. Thermal expansion amount calculation is performed on the tower drum temperature data based on the linear expansion coefficient of steel to obtain length change values for each section of the tower drum. Based on the length change value of each section of the tower drum, cumulative deformation analysis processing is performed to obtain the overall size deviation of the tower drum, and the overall size deviation of the tower drum is input into the thermal deformation compensation mechanism to perform compensation amount calculation processing to obtain the tower drum compensation parameter; The time sequence prediction processing is performed on the tower drum compensation parameter to obtain the future size state of the tower drum, and the matching verification processing is performed on the future size state of the tower drum according to the hoisting operation time plan to obtain the tower drum compensation parameter.

[0039] Specifically, the distributed temperature sensor is a sensor network capable of measuring temperature at different positions in space at the same time. The temperature of the bottom section, middle section and top section of the tower drum is collected and processed in real time by the distributed temperature sensor. The sensor converts temperature changes into electrical signals using thermocouple or thermistor principles. The bottom section sensor is installed at the tower drum foundation connection, the middle section sensor is installed at the tower drum middle flange position, and the top section sensor is installed at the tower drum top cabin connection. Each sensor collects temperature data at a frequency of once per minute to form temperature values of each section of the tower drum containing time stamp, position identification and temperature value. Temperature gradient analysis processing calculates the temperature change rule of the tower drum along the height direction based on the temperature values of each section of the tower drum. The analysis process calculates the temperature difference between adjacent measuring points, and then divides the height difference between the measuring points to obtain the temperature gradient value. The temperature gradient values of all measuring points are interpolated and fitted according to the height position to form a continuous tower drum temperature distribution curve. The tower drum temperature distribution curve describes the temperature change trend of the tower drum from the bottom to the top, reflecting the comprehensive influence of solar radiation, wind cooling, ground heat transfer and other factors on the temperature distribution of the tower drum.

[0040] The difference calculation processing performs subtraction operation on the temperature value of each height position in the tower drum temperature distribution curve and the environmental reference temperature. The environmental reference temperature is the standard temperature condition during the design of the tower drum, which is usually set to 20 degrees Celsius. The difference calculation result represents the deviation of the actual temperature of the tower drum relative to the reference temperature. A positive value indicates that the temperature is higher than the reference value, and a negative value indicates that the temperature is lower than the reference value. These deviation values constitute the tower drum temperature data. The thermal expansion amount calculation processing calculates the thermal deformation of the tower drum temperature data according to the linear expansion coefficient of steel. The linear expansion coefficient of steel is the ratio of the length change of the material to the product of the original length and the temperature change. For ordinary structural steel, this coefficient is 1.2 times 10 to the power of negative 5 per degree Celsius. In the calculation process, the temperature difference of each section of the tower drum is multiplied by the original length of the section and then multiplied by the linear expansion coefficient to obtain the length change of the section of the tower drum. The length change values of all sections are arranged according to the position of the tower drum from the bottom to the top to form the length change values of each section of the tower drum. The length change values of each section of the tower drum include the elongation or contraction of the bottom section, middle section and top section. The positive and negative values indicate the direction of elongation or contraction.

[0041] The cumulative deformation analysis process calculates the overall size change of the tower drum based on the length change values of each section of the tower drum. In the analysis process, the length change values of each section are vector superimposed in the direction of the tower drum height. The length change of the bottom section affects the relative positions of the middle section and the top section, and the length change of the middle section affects the relative position of the top section. The total displacement of the top of the tower drum relative to the bottom is obtained by step-by-step accumulation. The total displacement of the tower drum includes the height change in the vertical direction and the inclination deviation caused by uneven thermal expansion. These deviations will affect the assembly accuracy between the components of the wind turbine. The thermal deformation compensation mechanism is an algorithm that calculates the assembly correction amount based on the thermal deformation amount. The mechanism takes the overall size deviation of the tower drum as input, calculates the correction amount required for the installation position and angle based on the assembly requirements and tolerance range of the components of the wind turbine, and the correction amount includes horizontal displacement compensation, vertical displacement compensation, angle compensation, etc. These parameters constitute the tower drum compensation parameters.

[0042] The time series prediction process analyzes the future trend of the tower drum compensation parameters. This process uses time series analysis method to predict the future size state of the tower drum at a specific time point based on the historical temperature data and the change rule of the compensation parameters. The prediction process analyzes the periodicity of the tower drum temperature change, identifies the periodic characteristics such as daily temperature difference cycle and seasonal change, and then establishes a mathematical relationship model between temperature change and compensation parameter change. Based on the future temperature data from the weather forecast, the corresponding compensation parameter change is calculated to obtain the future size state of the tower drum. The future size state of the tower drum includes the tower drum height, inclination angle, and length of each section at the prediction time point. The matching verification process verifies the feasibility of the future size state of the tower drum based on the lifting operation time plan. The lifting operation time plan specifies the specific time arrangement of each lifting link, including tower drum section lifting time, nacelle installation time, blade installation time, etc. The verification process compares the predicted size state of the tower drum with the accuracy requirements of each lifting link. When the predicted size deviation exceeds the allowed range of lifting accuracy, the lifting time needs to be adjusted or additional compensation measures need to be taken. After verification and adjustment, the tower drum compensation parameters are obtained. The tower drum compensation parameters are compensation data obtained through time prediction and accuracy verification, which are directly used to guide the positioning control in the lifting operation.

[0043] In a specific embodiment, the process of performing step S105 can specifically include the following steps: performing coordinate measurement processing on the base center point of the lifting site by a GPS-RTK positioning system to obtain a base center coordinate, and performing lifting operation coordinate system establishment processing based on the base center coordinate to obtain a lifting reference coordinate; performing deviation correction calculation processing on the tower drum compensation parameters and the lifting reference coordinate to obtain a tower drum installation correction coordinate, and performing accuracy verification processing on the tower drum installation correction coordinate according to the equipment arrival state in the dynamic correction path to obtain an installation coordinate. The crane hook position is controlled and processed in space based on the installation coordinates to obtain a hook target position, and the hook target position is input into the crane posture control system for six-degree-of-freedom adjustment processing to obtain a lifting posture control instruction. The lifting posture control instruction is monitored and processed to obtain real-time lifting position data, and the real-time lifting position data is corrected for deviation according to the millimeter-level positioning accuracy requirement to obtain the equipment installation position.

[0044] Specifically, the GPS-RTK positioning system is a global positioning technology that can achieve centimeter-level positioning accuracy. The GPS-RTK positioning system is used to measure the coordinates of the basic center point of the lifting site. The RTK positioning principle is to use the differential correction signal sent by the ground reference station to correct the GPS satellite signal in real time, eliminating factors such as atmospheric delay and satellite orbit error. In the measurement process, the RTK receiver antenna is accurately aligned with the basic center point, and the satellite signals of multiple epochs are continuously observed. The three-dimensional coordinates of the basic center point, including east coordinate, north coordinate, and elevation coordinate, are calculated by the least squares method. These coordinate data form the basic center coordinates. The lifting operation coordinate system is established based on the basic center coordinates to build a special spatial coordinate system for wind power equipment lifting. In the establishment process, the basic center coordinates are taken as the coordinate origin, the axis direction of the tower drum design is taken as the positive direction of the Z axis, and the main wind direction is taken as the positive direction of the X axis. The Y axis direction is determined by the right-hand rule. The basic center coordinates in the geographic coordinate system are converted into coordinates in the lifting operation coordinate system to form the lifting reference coordinates. The lifting reference coordinates are the reference for all subsequent lifting positioning calculations and contain information such as coordinate origin position, three-axis direction definition, and coordinate system parameters.

[0045] The deviation correction calculation process performs numerical operation on the tower drum compensation parameters and the hoisting reference coordinates. The tower drum compensation parameters include correction values of horizontal offset, vertical offset, angle offset and the like caused by thermal deformation. The calculation process superimposes these offset values on the corresponding components of the hoisting reference coordinates. The horizontal offset corrects the X and Y coordinate components, the vertical offset corrects the Z coordinate component, and the angle offset corrects the direction of the coordinate system through a rotation matrix transformation. After the deviation correction, the tower drum installation correction coordinates considering the influence of thermal deformation are obtained. The precision verification process performs reliability test on the tower drum installation correction coordinates according to the equipment arrival state in the dynamic correction path. The equipment arrival state includes the cumulative deformation in the tower drum transportation process, the installation attitude deviation, the transportation time delay and the like. The verification process quantifies the influence of these factors on the installation precision as coordinate deviation values, superimposes these values on the tower drum installation correction coordinates, and evaluates whether the coordinate precision meets the hoisting requirements. When the coordinate precision meets the millimeter-level positioning requirements, the installation coordinates are confirmed. When the precision does not meet the requirements, the compensation parameters need to be adjusted for recalculation. The installation coordinates are the accurate installation positions of the tower drum after multiple corrections and verifications, and are directly used to guide the positioning control of the crane.

[0046] The spatial positioning control process calculates the target position of the crane hook based on the installation coordinates. The control process analyzes the center of gravity position of the tower drum and the distribution of the hoisting points, calculates the center of gravity coordinates according to the geometric size and mass distribution of the tower drum, and determines the relative position relationship between the hook and the center of gravity of the tower drum according to the hoisting scheme. The installation coordinates are taken as the target position of the tower drum, and the target position of the hook is calculated through coordinate transformation. The target position of the hook includes the accurate coordinates and attitude angles of the hook in the three-dimensional space, which describes the spatial position of the hook when reaching the ideal hoisting state. The six-degree-of-freedom adjustment process inputs the target position of the hook into the attitude control system of the crane for multi-dimensional motion control. The six degrees of freedom include three translational degrees of freedom and three rotational degrees of freedom. The translational degrees of freedom control the position of the hook in the X, Y and Z directions, and the rotational degrees of freedom control the rotation angle of the hook around the three coordinate axes. The adjustment process uses inverse kinematics algorithm to calculate the target angles of each joint of the crane. According to the mechanism parameters and motion constraints of the crane, the target position of the hook is decomposed into specific control parameters such as main arm pitch angle, auxiliary arm amplitude angle, rotation angle, lifting height, hook rotation angle and inclination angle. These parameters constitute the hoisting attitude control instructions.

[0047] The execution monitoring process tracks and feeds back the execution process of the lifting posture control instruction in real time. The monitoring process collects the actual motion state of the crane through the position sensors and angle sensors installed at each joint of the crane, including the actual angle of each joint, the actual position of the hook, the motion speed, and other parameters. The actual measurement values are compared with the target values in the control instruction, the execution deviation and motion trajectory are calculated, and real-time lifting posture data are formed. The deviation correction process analyzes and corrects the real-time lifting posture data according to the millimeter-level positioning accuracy requirement. The millimeter-level positioning accuracy requirement is that the hook position error is not more than 5 millimeters, and the angle error is not more than 0.1 degrees. The correction process uses a PID control algorithm to calculate the correction amount according to the position deviation and angle deviation, and continuously adjusts the motion parameters of the crane through closed-loop feedback control until the hook reaches the target position and the error meets the accuracy requirement. At this time, the hook position is the equipment installation position. The equipment installation position is the lifting position controlled and corrected in real time, which ensures that the wind power equipment can be accurately installed at the designed position.

[0048] In a specific embodiment, the process of performing the spatial positioning control of the hook position of the crane based on the installation coordinates can specifically include the following steps: Perform spatial deviation calculation processing on the current position of the hook of the crane based on the installation coordinates to obtain a three-dimensional displacement vector, and perform hook motion trajectory planning processing based on the three-dimensional displacement vector to obtain a target position of the hook; Input the target position of the hook into the inverse kinematics algorithm of the crane to perform joint angle calculation processing and obtain target angles of each joint, perform dynamic check processing on the target angles of each joint according to the load state of the crane to obtain checked joint angles; Perform crane boom pitch angle, rotation angle, and amplitude angle calculation processing based on the checked joint angles to obtain three-axis control parameters of the crane, and perform combination processing on the three-axis control parameters of the crane and the hook lifting control parameters to obtain a six-degree-of-freedom control matrix; Perform servo control instruction conversion processing on the six-degree-of-freedom control matrix to obtain a motor driving instruction set, perform boundary constraint processing on the motor driving instruction set according to the safety limit conditions of the crane to obtain the lifting posture control instruction.

[0049] Specifically, the spatial deviation calculation process performs a three-dimensional vector analysis on the current position of the crane hook according to the installation coordinates, which obtains the real-time coordinates of the hook through the position sensor installed on the crane, contains the specific position values of the hook in the X, Y, Z three directions, and then takes the installation coordinates as the target position, calculates the difference between the two positions through vector subtraction, and calculates the X coordinate of the target position minus the X coordinate of the current position to obtain the displacement component in the X direction, and similarly calculates the displacement components in the Y and Z directions, and the three displacement components form a three-dimensional displacement vector. The three-dimensional displacement vector describes the direction and distance that the hook needs to move from the current position to the target position, and the length of the vector represents the total moving distance, and the direction of the vector represents the spatial direction of the movement. The hook trajectory planning process designs the optimal motion path of the hook based on the three-dimensional displacement vector, considers the motion constraints and obstacle avoidance requirements of the crane in the planning process, and optimizes the straight motion trajectory into a continuous curve trajectory using a path smoothing algorithm to avoid impact on the equipment caused by sudden acceleration and deceleration. The planning algorithm decomposes the three-dimensional displacement vector into multiple intermediate path points, each path point contains position coordinates and motion speed, and all path points are connected in time sequence to form a continuous motion trajectory, and the end point of the trajectory is the target position of the hook. The target position of the hook is the arrival position after trajectory optimization, which contains three-dimensional coordinates and attitude angles at the time of arrival.

[0050] The crane inverse kinematics algorithm is a mathematical method for calculating the angles of each joint according to the position of the end effector, which takes the target position of the hook as input and solves the angle values of each joint according to the geometric parameters and kinematics equations of the crane mechanism. The algorithm process establishes a mathematical model of the crane, describes the geometric relationship and motion constraints between the joints, and then calculates the joint angle combination that satisfies the position constraints using an iterative solution method. The calculation process needs to consider multiple degrees of freedom of the crane, including the main arm pitch joint, the auxiliary arm amplitude joint, the rotation joint, the lifting joint, etc. The angle calculation of each joint needs to satisfy the geometric constraints and motion limitations of the mechanism to obtain the target angles of each joint. The target angles of each joint contain the specific angle values that each driven joint needs to rotate to, which directly determine the overall attitude of the crane and the position of the hook. The dynamic school verification process performs mechanical analysis and safety verification on the target angles of each joint according to the load state of the crane, which includes the weight, center of gravity position, wind load and other external forces of the hoisted equipment. The verification process calculates the torque and stress of each joint at the target angle, and compares the calculation results with the carrying capacity of the joint. When the joint carrying exceeds the safety limit, the target angle needs to be adjusted or the lifting scheme needs to be changed. The angle values confirmed to be safe after verification constitute the verified joint angles.

[0051] The crane three-axis control parameter calculation process extracts parameters of three main movement axes of the crane based on the checked joint angles. The three-axis control refers to controlling three main movement directions of the crane, including the boom luffing axis, the slewing axis and the luffing axis. The movement combination of the three axes determines the basic working posture of the crane. The calculation process extracts angle values corresponding to the three main axes from the checked joint angles. The boom luffing angle controls the inclination angle of the main boom relative to the horizontal plane. The slewing angle controls the rotation angle of the crane around the vertical axis. The luffing angle controls the angle of the auxiliary boom relative to the main boom. The three angle parameters constitute the crane three-axis control parameters. The hook lifting control parameter is an independent parameter for controlling the vertical movement of the hook, including the lifting height, the lifting speed, the acceleration and the like. The parameter is calculated according to the difference between the Z coordinate of the target position of the hook and the current Z coordinate. The combination process integrates the crane three-axis control parameters and the hook lifting control parameter to form a parameter matrix describing the complete movement state of the crane. The matrix includes six degrees of freedom control parameters corresponding to the X, Y and Z three translation directions and the rotation directions around the three coordinate axes. Each element in the matrix represents the control amount of the corresponding degree of freedom. The whole matrix constitutes a six-degree-of-freedom control matrix.

[0052] The servo control instruction conversion process converts the six-degree-of-freedom control matrix into specific instructions executable by the motors of the crane. The conversion process converts the angle, displacement, speed and the like in the control matrix into the rotation speed, rotation direction, torque and the like of the motor according to the drive system configuration of the crane. Each motor corresponds to one or more control parameters. The conversion algorithm calculates the specific movement instructions of the motor according to the mechanical parameters such as the transmission ratio and the reduction ratio of the motor. The drive instructions of all the motors constitute a motor drive instruction set. The motor drive instruction set includes the control instructions of all the drive motors of the crane. Each instruction specifies the movement mode and movement parameters of the corresponding motor. The boundary constraint process performs safety check and parameter limitation on the motor drive instruction set according to the safety limit conditions of the crane. The safety limit conditions include the maximum rotation angle, the maximum rotation speed, the maximum load and the like of each joint. The constraint process checks whether each motor instruction exceeds the safety range. When the instruction exceeds the limit, it is automatically adjusted to the maximum allowable value within the safety range. The instruction after the boundary constraint ensures the safety of the movement of the crane and forms the lifting posture control instruction. The lifting posture control instruction is the motor control instruction after complete calculation and safety check. It is directly sent to the control circuit of the crane to perform specific lifting actions.

[0053] The above describes the mountain wind power equipment transportation path planning and lifting control method in the embodiments of the present application. The mountain wind power equipment transportation path planning and lifting control system in the embodiments of the present application is described below. Please refer to Figure 2 The mountain wind power equipment transportation path planning and lifting control system in the embodiments of the present application includes one embodiment: The extraction module is configured to perform three-dimensional scanning on the mountainous road by the laser ranging array to obtain a road cross-section database, and to model the space occupation of the wind power blade according to the geometric parameters of the wind power blade to obtain blade swept envelope data. The planning module is configured to perform dynamic path planning on the road cross-section database and the blade swept envelope data by a terrain adaptability predictive control algorithm to obtain a blade transportation path, and to perform safety verification on the blade transportation path according to a stress constraint condition to obtain a safe transportation path. The monitoring module is configured to perform real-time monitoring on the blade transportation process by a sensor array to obtain blade state data, and to perform trajectory correction on the safe transportation path according to the blade state data to obtain a dynamically corrected path. The prediction module is configured to perform temperature collection on each section of the tower by a temperature sensor to obtain tower temperature data, and to perform size prediction on the tower temperature data according to a thermal deformation compensation mechanism to obtain tower compensation parameters. The control module is configured to perform coordinate acquisition on the hoisting site by a positioning system to obtain hoisting reference coordinates, and to control the hoisting posture according to the tower compensation parameters and the dynamically corrected path to obtain an equipment installation position.

[0054] The above Figure 2 The mountainous wind power equipment transportation path planning and hoisting control system in the embodiment of the present application is described in detail from the perspective of modular functional entities, and the mountainous wind power equipment transportation path planning and hoisting control device in the embodiment of the present application is described in detail from the perspective of hardware processing.

[0055] Referring Figure 3 In the embodiment of the present application, a mountainous wind power equipment transportation path planning and hoisting control device is also provided, which can be a server, and the internal structure thereof can be as shown in Figure 3 The mountainous wind power equipment transportation path planning and hoisting control device comprises a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is configured to provide computing and control capabilities. The memory of the mountainous wind power equipment transportation path planning and hoisting control device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the mountainous wind power equipment transportation path planning and hoisting control device is configured to store the corresponding data in the embodiment. The network interface of the mountainous wind power equipment transportation path planning and hoisting control device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above method.

[0056] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the mountain wind power equipment transportation path planning and hoisting control device to which the scheme of the present application is applied.

[0057] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium, and can also be a volatile computer readable storage medium, and instructions are stored in the computer readable storage medium, and when the instructions are run on a computer, the computer executes the steps of the mountain wind power equipment transportation path planning and hoisting control method.

[0058] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, system and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0059] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical scheme of the present application or the part that contributes to the prior art essentially or the whole or part of the technical scheme can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a mountain wind power equipment transportation path planning and hoisting control device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0060] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for transport route planning and hoisting control of mountain wind power equipment, characterized in that: The method comprises: The mountain roads are scanned in three dimensions using a laser ranging array to obtain a road cross-section database. The blade space occupancy is modeled based on the geometric parameters of the wind turbine blades to obtain blade sweep envelope data. Performing dynamic path planning processing on the road cross-section database and the blade sweep envelope data using a terrain adaptability predictive control algorithm to obtain a blade transportation path, and performing safety verification processing on the blade transportation path based on stress constraint conditions to obtain a safe transportation path; The blade transportation process is monitored in real time by a sensor array to obtain blade status data, and the safe transportation path is corrected according to the blade status data to obtain a dynamic correction path; The temperature of each section of the tower is collected and processed by a temperature sensor to obtain tower temperature data, and the size of the tower temperature data is predicted and processed according to the thermal deformation compensation mechanism to obtain tower compensation parameters; The positioning system is used to obtain the coordinates of the hoisting site to obtain the hoisting reference coordinates, and the hoisting posture is controlled according to the tower compensation parameters and the dynamic correction path to obtain the equipment installation position.

2. The method for transport route planning and hoisting control of mountain wind power equipment according to claim 1, characterized in that: The three-dimensional scanning of mountain roads by a laser ranging array is performed to obtain a road cross-section database, and blade space occupancy is modeled based on wind turbine blade geometric parameters to obtain blade sweep envelope data, including: Laser scanning sampling is performed at intervals of 10 meters along the mountain road to obtain road sampling point cloud data, and cross-sectional profile extraction is performed based on the road sampling point cloud data to obtain road cross-sectional profile data at each sampling point; Sorting and storing the road cross-section profile data according to the road mileage pile numbers to obtain the road cross-section database; A blade geometric model is constructed based on the wind turbine blade length, blade width, and blade thickness parameters to obtain a three-dimensional blade geometric model; a dynamic swing range of the blade three-dimensional geometric model is calculated based on the turning radius of the transport vehicle to obtain the blade sweep envelope data; The blade sweep envelope data is subjected to space coordinate transformation processing to obtain blade sweep envelope data that matches the road cross-section database.

3. The method for transport route planning and hoisting control of mountain wind power equipment according to claim 1, characterized in that: The method includes performing dynamic path planning processing on the road cross-section database and the blade sweep envelope data using a terrain adaptability predictive control algorithm to obtain a blade transportation path, and performing safety verification processing on the blade transportation path according to stress constraint conditions to obtain a safe transportation path, including: Inputting the road cross-section database and the blade sweep envelope data into a terrain adaptability prediction control algorithm for spatial matching analysis and processing to obtain a path feasibility matrix, and performing multi-objective optimization calculation processing based on the path feasibility matrix to obtain a candidate path set; Calculating the curvature radius of each path in the candidate path set to obtain path curvature parameters, and calculating the blade root stress of the path curvature parameters according to the blade length and the elastic modulus of the blade material to obtain path stress distribution data; Performing stress threshold comparison processing based on the path stress distribution data to obtain a stress safety evaluation result, screening the paths that meet the stress constraint conditions to obtain the blade transportation path; The blade transport path is subjected to path smoothing optimization processing to obtain an optimized transport path, and the optimized transport path is subjected to safety margin verification processing according to a mountain crosswind influence factor to obtain the safe transport path.

4. The method for transport route planning and hoisting control of mountain wind power equipment according to claim 1, characterized in that: The blade transportation process is monitored in real time by a sensor array to obtain blade status data, and the safe transportation path is corrected according to the blade status data to obtain a dynamic correction path, including: The deformation state of the blade is collected and processed in real time by strain sensors installed at the root, middle and tip of the blade to obtain blade strain data, and bending stress is calculated and processed based on the blade strain data to obtain the real-time stress distribution of the blade; Comparing and analyzing the real-time stress distribution of the blade with a preset stress threshold to obtain a stress overlimit warning signal, and performing position correlation processing on the stress overlimit warning signal according to the current position coordinates of the transport vehicle to obtain the blade status data; Based on the blade status data, risk sections of the safe transport path are identified to obtain high-risk path nodes, and the high-risk path nodes are input into a path replanning algorithm to generate alternative trajectories to obtain a corrected trajectory plan; The corrected trajectory plan is subjected to path feasibility verification processing to obtain a verified corrected plan, and the verified corrected plan is optimized according to the transportation time cost and safety factor to obtain the dynamic corrected path.

5. The method for transport route planning and hoisting control of mountain wind power equipment according to claim 1, characterized in that: The temperature of each section of the tower is collected and processed by a temperature sensor to obtain tower temperature data, and the tower temperature data is subjected to size prediction processing according to a thermal deformation compensation mechanism to obtain tower compensation parameters, including: The temperature of the bottom, middle and top sections of the tower is collected and processed in real time by distributed temperature sensors to obtain the temperature value of each section of the tower. The temperature gradient analysis is performed based on the temperature value of each section of the tower to obtain the tower temperature distribution curve; Performing a difference calculation process on the tower temperature distribution curve and the ambient reference temperature to obtain the tower temperature data, and performing a thermal expansion calculation process on the tower temperature data according to the linear expansion coefficient of steel to obtain the length change value of each section of the tower; Performing cumulative deformation analysis based on the length change values ​​of each section of the tower to obtain the overall dimensional deviation of the tower, and inputting the overall dimensional deviation of the tower into the thermal deformation compensation mechanism to calculate the compensation amount to obtain the tower compensation parameter; The tower compensation parameters are subjected to time series prediction processing to obtain the future size state of the tower, and the future size state of the tower is matched and verified according to the hoisting operation time plan to obtain the tower compensation parameters.

6. The method for transport route planning and hoisting control of mountain wind power equipment according to claim 1, characterized in that: The coordinate acquisition process of the hoisting site is performed by the positioning system to obtain the hoisting reference coordinates, and the hoisting posture is controlled and processed according to the tower compensation parameters and the dynamic correction path to obtain the equipment installation position, including: The coordinate measurement process of the foundation center point at the hoisting site is performed by the GPS-RTK positioning system to obtain the foundation center coordinates, and the hoisting operation coordinate system is established based on the foundation center coordinates to obtain the hoisting reference coordinates; Perform deviation correction calculation processing on the tower compensation parameters and the hoisting reference coordinates to obtain tower installation correction coordinates, and perform accuracy verification processing on the tower installation correction coordinates according to the equipment arrival status in the dynamic correction path to obtain installation coordinates; Performing spatial positioning control processing on the crane hook position based on the installation coordinates to obtain a hook target position, inputting the hook target position into the crane attitude control system for six-degree-of-freedom adjustment processing to obtain a lifting attitude control instruction; The hoisting posture control instruction is executed and monitored to obtain real-time hoisting posture data, and the real-time hoisting posture data is subjected to deviation correction processing according to the millimeter-level positioning accuracy requirement to obtain the equipment installation position.

7. The method for transport route planning and hoisting control of mountain wind power equipment according to claim 6, characterized in that: The method of performing spatial positioning control processing on the crane hook position based on the installation coordinates to obtain a hook target position, inputting the hook target position into the crane attitude control system for six-degree-of-freedom adjustment processing to obtain a lifting attitude control instruction includes: Performing spatial deviation calculation processing on the current position of the crane hook according to the installation coordinates to obtain a three-dimensional displacement vector, and performing hook motion trajectory planning processing based on the three-dimensional displacement vector to obtain the target position of the hook; Inputting the hook target position into the crane kinematics inverse algorithm to perform joint angle calculation processing to obtain the target angle of each joint, and performing dynamic verification processing on the target angle of each joint according to the load state of the crane to obtain the verified joint angle; Calculate the crane boom pitch angle, slew angle, and luffing angle based on the verified joint angles to obtain three-axis control parameters of the crane, and combine the three-axis control parameters of the crane with the hook lifting control parameters to obtain a six-degree-of-freedom control matrix; The six-degree-of-freedom control matrix is ​​subjected to servo control instruction conversion processing to obtain a motor drive instruction set, and the motor drive instruction set is subjected to boundary constraint processing according to a crane safety limit condition to obtain the hoisting posture control instruction.

8. A mountain wind power equipment transportation path planning and hoisting control system, characterized in that: The method for planning and controlling the transportation path of mountain wind power equipment and the hoisting control system according to any one of claims 1 to 7 is used to implement the method, wherein the transportation path planning and hoisting control system for mountain wind power equipment comprises: The extraction module is used to perform three-dimensional scanning of mountain roads using a laser ranging array to obtain a road cross-section database, model the blade space occupancy based on the geometric parameters of the wind turbine blades, and obtain blade swept envelope data; a planning module, configured to perform dynamic path planning processing on the road cross-section database and the blade sweep envelope data using a terrain adaptability predictive control algorithm to obtain a blade transportation path, and perform safety verification processing on the blade transportation path according to stress constraint conditions to obtain a safe transportation path; A monitoring module is used to monitor the blade transportation process in real time through a sensor array to obtain blade status data, and to perform trajectory correction processing on the safe transportation path according to the blade status data to obtain a dynamically corrected path; A prediction module is used to collect and process the temperature of each section of the tower through a temperature sensor to obtain tower temperature data, and perform size prediction processing on the tower temperature data according to a thermal deformation compensation mechanism to obtain tower compensation parameters; The control module is used to obtain the coordinates of the hoisting site through the positioning system to obtain the hoisting reference coordinates, control the hoisting posture according to the tower compensation parameters and the dynamic correction path, and obtain the equipment installation position.

9. A mountain wind power equipment transportation path planning and hoisting control device, characterized in that: It includes a memory and a processor, the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the mountain wind power equipment transportation path planning and hoisting control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is enabled to execute the mountain wind power equipment transportation path planning and hoisting control method according to any one of claims 1 to 7.

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